Title :
De Novo Design of Potential RecA Inhibitors Using MultiObjective Optimization
Author :
Sengupta, Soumi ; Bandyopadhyay, Sanghamitra
Author_Institution :
Machine Intell. Unit, Indian Stat. Inst., Kolkata, India
Abstract :
De novo ligand design involves optimization of several ligand properties such as binding affinity, ligand volume, drug likeness, etc. Therefore, optimization of these properties independently and simultaneously seems appropriate. In this paper, the ligand design problem is modeled in a multiobjective using Archived MultiObjective Simulated Annealing (AMOSA) as the underlying search algorithm. The multiple objectives considered are the energy components similarity to a known inhibitor and a novel drug likeliness measure based on Lipinski´s rule of five. RecA protein of Mycobacterium tuberculosis, causative agent of tuberculosis, is taken as the target for the drug design. To gauge the goodness of the results, they are compared to the outputs of LigBuilder, NEWLEAD, and Variable genetic algorithm (VGA). The same problem has also been modeled using a well-established genetic algorithm-based multiobjective optimization technique, Nondominated Sorting Genetic Algorithm-II (NSGA-II), to find the efficacy of AMOSA through comparative analysis. Results demonstrate that while some small molecules designed by the proposed approach are remarkably similar to the known inhibitors of RecA, some new ones are discovered that may be potential candidates for novel lead molecules against tuberculosis.
Keywords :
bioinformatics; diseases; drugs; genetic algorithms; medical computing; microorganisms; molecular biophysics; proteins; simulated annealing; AMOSA; LigBuilder; Lipinski rule of five; Mycobacterium tuberculosis; NEWLEAD; RecA inhibitors; RecA protein; archived multiobjective simulated annealing; binding affinity; de novo ligand design; drug design; drug likeness; energy component similarity; genetic algorithm-based multiobjective optimization; ligand volume; nondominated sorting genetic algorithm-II; search algorithm; variable genetic algorithm; Algorithm design and analysis; Drugs; Genetic algorithms; Inhibitors; Proteins; Simulated annealing; De novo ligand design; Mycobacterium tuberculosis; genetic algorithm; multiobjective optimization; oral bioavailability; rational drug design.; simulated annealing; Algorithms; Bacterial Proteins; Computational Biology; Drug Design; Enzyme Inhibitors; Ligands; Models, Genetic; Models, Molecular; Mycobacterium tuberculosis; Protein Binding; Rec A Recombinases;
Journal_Title :
Computational Biology and Bioinformatics, IEEE/ACM Transactions on
DOI :
10.1109/TCBB.2012.35